{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beyond 1\n",
    "\n",
    "Rather than starting with data from Chicago, start with an empty data frame, and use a `for` loop to load data from all three cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>maxtemp</th>\n",
       "      <th>mintemp</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-11 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11 06:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11 09:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11 12:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     maxtemp  mintemp     city\n",
       "date_time                                     \n",
       "2018-12-11 00:00:00        1       -2  Chicago\n",
       "2018-12-11 03:00:00        1       -2  Chicago\n",
       "2018-12-11 06:00:00        1       -2  Chicago\n",
       "2018-12-11 09:00:00        1       -2  Chicago\n",
       "2018-12-11 12:00:00        1       -2  Chicago"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame()\n",
    "\n",
    "all_dfs = []\n",
    "\n",
    "for city_stem in ['chicago,il', 'los+angeles,ca', 'boston,ma']:\n",
    "    new_df = pd.read_csv(f'../data/{city_stem}.csv', \n",
    "                 usecols=[0, 1,2],\n",
    "                 header=0,\n",
    "                 names=['date_time','maxtemp', 'mintemp'],\n",
    "                parse_dates=['date_time'],\n",
    "                index_col=['date_time'])\n",
    "    new_df['city'] = city_stem.split(',')[0].replace('+', ' ').title()\n",
    "    all_dfs.append(new_df)\n",
    "\n",
    "df = pd.concat(all_dfs)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beyond 2\n",
    "\n",
    "For each city, calculate the mean and median for `mintemp` and `maxtemp`. Are they the same (or even close)? If they're different, in which direction were they pulled?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">mintemp</th>\n",
       "      <th colspan=\"2\" halign=\"left\">maxtemp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Boston</th>\n",
       "      <td>-3.142857</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2.868132</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>-5.076923</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-0.736264</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>10.637363</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.054945</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mintemp           maxtemp       \n",
       "                  mean median       mean median\n",
       "city                                           \n",
       "Boston       -3.142857   -3.0   2.868132    2.0\n",
       "Chicago      -5.076923   -4.0  -0.736264    0.0\n",
       "Los Angeles  10.637363   11.0  17.054945   16.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('city')[['mintemp', 'maxtemp']].agg(['mean', 'median'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beyond 3\n",
    "\n",
    "Create a line plot showing the minimum temperatures in each city. The x axis should show dates, the y axis should show temperatures, and each line should represent a different city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Boston         Axes(0.125,0.11;0.775x0.77)\n",
       "Chicago        Axes(0.125,0.11;0.775x0.77)\n",
       "Los Angeles    Axes(0.125,0.11;0.775x0.77)\n",
       "Name: mintemp, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('city')['mintemp'].plot.line(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
